"If at first the idea is not absurd, then there is no hope for it"
— Albert Einstein
Censorship in US films and series
Proposed by Luis
During the last years many important US directors and actors are developing very high quality films and series directly for television. One of the reasons behind this change is to avoid censorship in cinemas. If a film wants to be in an US cinema, first censors label it according strict standards. For example, some European films are getting the qualification of X in US when they are clearly not pornographic; films with this label can only be projected in an X cinema with the consequent lack of advertisement and being surrounded by actual pornographic films. On the other hand, in cable TV there is not "public censorship" in US, since it is understood that the censorship is established by each family through a "private censorship".
It could be interesting to study the effect of public and private censorship through theoretical models, simulations and/or real data.
Interested participants: Luis, Zoran
Understanding Smart Grids
Proposed by Iván
Smart grids (SGs) are systems of systems whose goal is to 'smartly' manage electricity consumption and production by promoting synergetic interactions among people, technology and natural resources. E.g. citizens can currently use photovoltaic panels to exploit solar energy producing electricity that can be directly used to satisfy their needs or traded with electricity operators. Engineering SGs, however, faces so-called inherent as well as epistemic complexity. The former is related to all dynamic relationships among customers, prosumers (those who consume but also produce electricity), network operators and markets; whereas the latter refers to our limited understanding about SG's issues, e.g. assessing whether some architectural designs might be more sustainable than others. During this workshop, it might be interesting to look at some ways to address this architectural issue.
The Big Brother and I robot, fiction or truth?
Proposed by Rubén J. Requejo
The romance 1984 from George Orwell is colloquially said to describe a distopic future imagined by the author in the after-World War period. However, most of the technologies described in the book were already availabe at the time the romance was written. Furthermore, new advances and developments in information, communication and computation technologies may nowadays allow for a much higher level of control as that imagined by Orwell. On the other side, they also allow for new organizational ways, both centralized and decentralized, which are growing in the "smart cities" in order for authomatized systems to help us improve our quality of life. Both developmental ways have common points and distinctive features which may be identified in order to make a clear statement on the point at which society is nowadays. Are we nearer to the Big Brother or to the I robot paradigms?
I propose to make a survey of the present state on the development of the new technologies involved in the technological change of society, including the controlled/uncontrolled spread of sensors across cities, the Google vs Wikileaks models of information use and the past/present/future connotations of the politics involved on them. If possible, it would be interesting to answer to three questions: Is it possible to make a complete model for simulating society? Is it possible to use it to enhance our quality of life as well as individual (and hence collective) freedom? May such a model be used to control society in a tyranic way?
Furthermore, a fourth question could also be introduced regarding our near future: How is quantum computation going to shape the development of society?
Navigating social change: a Natural Language Processing approach to detecting and forecasting change in social systems
Proposed by Marta
In Niklas Luhmann’s view social systems do not ‘comprise of’ people, but of distinction-based meanings carried by their communication. They are not collections of human agents but, rather, meaningful phenomena that emerge and self-produce between them. If you want to know how a social system is changing, you need, then, to observe what is happening to distinctions which shape communication within it. I propose to employ Natural Language Processing methods to track change in social systems and predict its future course. It will be done out of a collection of real texts produced by the social systems in question.
Interested participants: Zoran
R-Genes, How many are too many?
Proposed by Carlos A Lugo
Current Biological paradigms regarding plants and their resistance to the ever evolving parasites biochemical invading strategies are unable to give useful estimates on how many
R-genes are necessary and sufficient to keep a given strain of the pathogen at bay for a given period of time T.
By combining techniques of stochastic dynamics, perturbation theory and recent advances in dynamical processes in complex networks (competition and evolution of the parasite's effector network vs the host R genes and target network) I hope to develop a generic theory for the duration of the transients between resistance overcoming by the pathogen in a given population of hosts.
Basically I want apply the ideas here:
* Evolutionary dynamics on networks of selectively neutral genotypes: Effects of topology and sequence stability, Jacobo Aguirre, Javier M. Buldú, and Susanna C. Manrubia, Phys. Rev. E, 80, 066112 (2009).
* Successful strategies for competing networks, J. Aguirre, D. Papo, J. M. Buldú Nature Physics 9, 230–234 (2013) doi:10.1038/nphys2556
To the "model" presented in The plant immune system, Jonathan D. G. Jones and Jeffery L. Dangl, Nature 444, 323-329 (16 November 2006) doi:10.1038/nature05286
This seems to be more or less straightforward. I have some ideas for this project, however, it has been devised to specially for the workshop.
Other projects I'm working in can be found at http://calugo.github.io
Homophily vs contagion
Proposed by Anxo
Studies of social networks are finding many applications in fields such as medicine, where claims like "if your friends are overweight the chances are that you are overweight too" appear every so often. However, the assumption behind those claims is that people influence each other, and this is not clear as in fact this is like the chicken or egg dilemma (or, in disguise, the correlation vs causation problem). Does people influence each other, so that your overweight friends make you eat more and lead you to obesity? Or is it the other way round, namely you hang out (physically or virtually) with people who are like you? This is a very big issue nowadays and one that is hard to tackle from real data obtained from, e.g., social networks. On the other hand, to date and to my knowledge, there is not any experimental procedure or setup that clearly allows to disentangle both effects. Therefore, a very important research avenue would be to design such an experiment or else to come up with algorithms that allow to decide what causes what in real data.
Interested participants: Zoran, Brais, Francesca, Alberto, Luis
Proposed by Luis
The process of writing horoscopes is not simple. On one hand, the writer has to choose general enough concepts and adjectives that can be applied to any reader and, on the other hand, the reader has to feel good and not cheated reading it. This "art" can be found not only in horoscopes but also in (fake) tests of personality all over the Internet. These techniques becomes really serious when it leaves the pure entertainment to be applied to marketing or even politics.
It would be interesting to study which concrete concepts, adjectives, expressions are repeated in these kind of texts and to be able to design an algorithm that creates new "horoscopes". This project would be in the intersection of Machine Learning, Linguistic and Psychology.
Interested participants: Luis, Leto, Alberto, Zoran
Quantifying the complexity of complex systems
Proposed by Zoran
Complex system is an ensemble of simple units that interact and generate emergent behavior that serves a certain function (eg. power grids, airports, words, genes, neurons etc). The number and functionality of these units vary drastically: we have 1011 neurons in our bodies, all of which are very simple, but they collectively generate amazing phenomena such as eye vision and jealousy. On the other hand, the number of airports in the world is on the order of 104, yet each airport is itself a rather complex entity. What contributes more to the functionality of a given complex system, the number of individual units or their complexity? Is there a way to examine this problem systematically, both at modeling level and at experimental level (data on real complex systems)? Say we call N the number of units and e the "functionality" of an isolated unit (meaning for example the number of discrete attainable states). How does the system's global functionality F varies with N and e, in particular, how dos it behave with increase of N and decrease of e? What is better: many simple units or few complex ones? This could develop into a major study of complexity of complex systems, answering some long-standing questions in the field.
Patterns detection in spatial networks
Proposed by Claire
What make a road network recognizable at a glance? What are the geometrical and topological properties making us know when we are confronted to a railway network, an hydraulic network or a streets network? Studying the elementary geometry and topology of such networks we should be able to automatize the characterization of spatial networks. Furthermore, in one specific type of complex spatial network one can observe different type of patterns. For instance, in cities, the study of the motifs should let us know if the extracted network is from the center of a city, the suburbs or the countryside.
The idea of this project is to understand the minimal properties to qualify a space network. The outcome of the research could be the development of a tool identifying the nature of an input network.
Following that we could
- generate random networks to see how the tool react ;
- extend the methodology to social networks, focusing more on the topological aspect ;
- extend the methodology to 3D spatial networks.
Networks beyond Graphs
Proposed by Fernando
We are all familiar with the notion of correlation between two elements. This concept measures how much two variables have in common, how much information they share, and therefore how much one can be used to predict the other. These notions allow us to formulate useful statements like “if X increases then Y is likely to increase too”, which lay the basis for most of the technology that surrounds us today.
Interested participants: Zoran
Modelling participatory sense-making
Proposed by Viktoras
Sense-making "is a motivated, continuous effort to understand connections (which can be among people, places, and events) in order to anticipate their trajectories and act effectively in relation to them" -- i.e. the process leading to understanding (making-sense of) the world. Partipatory sense-making introduces a social dimension to understanding the world by emphasizing the influence of mutual interaction among sense-making agents and their individual views which at the extreme may not correlate to 'objective reality' at all.
Sense-making is important for cognition and intelligence, while the concept of participatory sense-making may contribute to understanding social dynamics and the nature of collective intelligence. Furthermore, if subscribing to the view to individual cognitive system as a complex system of interacting agents on lower scales (e.g. neurons in case of a brain), understanding the mechanism of participatory sense-making may shed a light on the enigma of individual cognition.
With this project we would like to design, implement and test a pilot computational model of participatory sense-making using message passing algorithm based on Java programming language. The detailed project proposal can be downloaded as a separate document (4 pages).
Related WWCS2015 project proposals: Understanding Smart Grids, Quantifying the complexity of complex systems, Networks beyond Graphs.